Sensor Location Optimization for Effective and Robust Beamforming

In many applications, the sensor array’s geometrical layout is assumed to be fixed and given in advance. However, it is possible to change the geometrical layout of the array including adjacent sensor spacing and these additional spatial degrees of freedom (DOFs) can be exploited to improve the performance in terms of either beamforming or direction finding or both. With the development of compressive sensing (CS) or the sparsity maximization framework, a new CS-based framework with a theoretically optimum solution (due to convex nature of the formulation) has been developed for general sensor location optimization, with robustness against various array model errors considered too. In this talk, the CS-based framework for sensor location optimization will be presented for effective and robust beamforming.